The present invention relates generally to digital imaging and digital image enhancement techniques. More particularly, the invention relates to a novel technique for characterizing noise in a digital image and for compensating for or reducing noise in a processed image.
Many techniques are known and are presently in use for generating digital image data. Such techniques range from simple charge coupled device apparatus, such as digital cameras, to much more complex imaging systems, such as those used for part inspection and medical diagnostics purposes. In all of these systems, a matrix of discrete picture elements or pixels is made up of individual values over a range of intensities. The matrix may also include colors, typically a combination of three base colors. The raw image data acquired by the imaging system may be processed to clarify the image, enhance image features, or otherwise improve the image quality from various points of view. In general, the goal of image enhancement and quality improvement is to provide more useful images, typically more clear or in some way satisfying images for the user.
By way of example, in the medical imaging context, a number of imaging modalities are employed. The modalities are typically based on vastly different imaging physics, and include magnetic resonance imaging (MRI) systems, computed tomography (CT) imaging systems, ultrasound imaging systems, X-ray imaging systems, positron emission tomography (PET) systems, electron beam imaging systems, tomosynthesis systems, and so forth. A scanner or other image acquisition system typically acquires raw image data which is then processed to form a useful set of data for image reconstruction and viewing. The systems typically include on-board processing capabilities for certain processing, while other processing may be performed in subsequent steps, generally referred to as post-processing. In all cases, image enhancement may be an ultimate goal, with raw, partially processed or enhanced image data being stored for later retrieval, reconstruction, transmission, and so forth.
Acquired image data from all types of imaging systems typically contain noise. Noise may result from a wide variety of sources, typically from the various components used to acquire the image data, but may also be a function of the physics of the system, the nature of the subject being imaged, and so forth. Typical image noise may be a mixture of random point noise, which may also be referred to as spike noise, and patterned noise. Modalities such as X-ray imaging and optical imaging, where image data is directly acquired exhibit such noise in a readily visible manner. However, imaging methods requiring reconstruction, such as MRI, CT, ultrasound, and so forth, convert point or spike noise into splotches or small streaks and thereafter the point noise is usually hidden with the patterned noise. In either of these cases, it is desirable that the point noise and patterned noise be detected and appropriately mitigated.
To eliminate spike noise, one class of existing methods uses median filtering or adaptive variation. Another class of methods uses temporal averaging. Methods designed to mitigate patterned noise do not adequately mitigate point noise, however, without blurring or decreasing the contrast of the useful information in the processed image.
There is a need therefore, for an improved technique for reducing both random noise points (spike noise) and patterned noise in the same image. There is a particular need for a technique which is easily implemented, computationally efficient, and which offers options for image enhancement and for time optimization.